ukb/00_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "64b97cbb-8aa0-4c97-90d1-981a8e4d6a86",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from os.path import join\n",
    "import xarray as xr\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "from sklearn.preprocessing import quantile_transform"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f919f71d-353b-431a-b352-4cd88134115f",
   "metadata": {},
   "source": [
    "# Select individuals based on genetic information and extract meta information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "49da20d0-fa27-43d9-a1b4-41a9d35efe2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "path_pheno = \"/u/project/sriram/ukbiobank/33127/ukb21970.csv\"\n",
    "path_admixture = \"00_data/supervised_with_sire.txt\" # output from SCOPE program"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0bdf5070-eb6c-4024-bbc3-5b9a70ececcf",
   "metadata": {},
   "outputs": [],
   "source": [
    "col_age = \"21003-0.0\"\n",
    "col_sex = \"31-0.0\"\n",
    "col_pcs = [f\"22009-0.{i}\" for i in range(1, 41)]\n",
    "col_report_ethnic = \"21000-0.0\"\n",
    "col_id = \"eid\"\n",
    "\n",
    "cols = [col_id, col_age, col_sex, col_report_ethnic] + col_pcs\n",
    "col_names = [\"ID\", \"AGE\", \"SEX\", \"SIRE_ID\"] + [f\"PC{i + 1}\" for i in range(len(col_pcs))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d92b4363-e55a-465a-9dea-ce92f9e3babb",
   "metadata": {},
   "outputs": [],
   "source": [
    "pheno = pd.read_csv(path_pheno, usecols=cols)[cols]\n",
    "pheno.columns = col_names\n",
    "pheno.ID = pheno.ID.astype(str)\n",
    "pheno = pheno.set_index(\"ID\")\n",
    "\n",
    "admixture = pd.read_csv(path_admixture, index_col=0, sep='\\t')\n",
    "admixture.index = admixture.index.astype(str)\n",
    "\n",
    "pheno = pd.merge(pheno, admixture, left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "22ea4aee-82c3-49e6-a3fb-540327b88622",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4327 admixed individuals were selected\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2b7742965ca0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 450x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "id_eur = pheno[pheno.SIRE == \"British\"].index\n",
    "id_afr = pheno[pheno.SIRE == \"African\"].index\n",
    "id_eas = pheno[pheno.SIRE == \"Chinese\"].index\n",
    "id_other = list(set(pheno.index) - set(id_eur) - set(id_afr) - set(id_eas))\n",
    "\n",
    "id_admix = pheno.index[(pheno.AFR > 0.05) & (pheno.EUR > 0.05) & (pheno.EAS < 0.05) & (pheno.SAS < 0.05)]\n",
    "print(f\"{len(id_admix)} admixed individuals were selected\")\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(3, 3), dpi=150)\n",
    "for id_pop, label in zip([id_eur, id_afr, id_eas, id_other, id_admix], [\"EUR\", \"AFR\", \"EAS\", \"OTHERS\", \"ADMIX\"]):\n",
    "    if label == \"EUR\":\n",
    "        id_pop = np.random.choice(id_pop, 5000, replace=False)\n",
    "    zorder = 0 if label == \"OTHERS\" else 10\n",
    "    if label == \"ADMIX\":\n",
    "        zorder = 20\n",
    "    ax.scatter(pheno.loc[id_pop, \"PC1\"], pheno.loc[id_pop, \"PC2\"], label=label, alpha=0.5, s=3, zorder=zorder)\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "931faadc-c5c5-450c-89bb-9a4ca8766e5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savetxt(\"00_data/admix_indiv.txt\", np.vstack([id_admix.values, id_admix.values]).T, delimiter='\\t', fmt=\"%s\")\n",
    "pheno.loc[id_admix, :].to_csv(\"00_data/meta_info.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cffa300c-9cb4-4978-9f43-f9ee5ade16b7",
   "metadata": {},
   "source": [
    "# Extract trait phenotype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5308d7ce-5586-4f4a-b375-f160f062a660",
   "metadata": {},
   "outputs": [],
   "source": [
    "# extract phenotype from another file (because in another UKB application)\n",
    "col_dict = {\n",
    "    \"21003-0.0\": \"AGE\",\n",
    "    \"31-0.0\": \"SEX\",\n",
    "    \"21000-0.0\": \"SIRE\",\n",
    "    \"eid\": \"ID\",\n",
    "    \"30897-0.0\": \"dilution_factor\",\n",
    "    \"30690-0.0\": \"cholesterol\",\n",
    "    \"30780-0.0\": \"ldl_direct\",\n",
    "    \"30760-0.0\": \"hdl_cholesterol\"\n",
    "}\n",
    "\n",
    "path_pheno = \"/u/project/sriram/ukbiobank/33127/ukb39967.enc_ukb.converted2.csv\"\n",
    "\n",
    "trait_pheno = pd.read_csv(path_pheno, usecols=col_dict.keys()).rename(columns=col_dict)\n",
    "trait_pheno.ID = trait_pheno.ID.astype(str)\n",
    "trait_pheno = trait_pheno.set_index(\"ID\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d65afd37-fc9e-4944-b6ca-759ce7fa5857",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "## SEX"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
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     "output_type": "display_data"
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AGE</th>\n",
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       "      <td>Caribbean</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "         AGE  SEX  SIRE_ID      PC1      PC2      PC3      PC4      PC5  \\\n",
       "5841951   55    0   4001.0  344.109  64.2523 -8.57264  5.05607  1.01965   \n",
       "\n",
       "              PC6      PC7  ...       EUR       SAS       SIRE  SEX_trait  \\\n",
       "5841951  0.665122 -3.47168  ...  0.106441  0.012949  Caribbean        NaN   \n",
       "\n",
       "         SIRE_trait  AGE_trait  cholesterol  hdl_cholesterol  ldl_direct  \\\n",
       "5841951         NaN        NaN          NaN              NaN         NaN   \n",
       "\n",
       "         dilution_factor  \n",
       "5841951              NaN  \n",
       "\n",
       "[1 rows x 55 columns]"
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     "metadata": {},
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    {
     "data": {
      "text/markdown": [
       "## AGE"
      ],
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       "<IPython.core.display.Markdown object>"
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "         AGE  SEX  SIRE_ID      PC1      PC2      PC3      PC4      PC5  \\\n",
       "5841951   55    0   4001.0  344.109  64.2523 -8.57264  5.05607  1.01965   \n",
       "\n",
       "              PC6      PC7  ...       EUR       SAS       SIRE  SEX_trait  \\\n",
       "5841951  0.665122 -3.47168  ...  0.106441  0.012949  Caribbean        NaN   \n",
       "\n",
       "         SIRE_trait  AGE_trait  cholesterol  hdl_cholesterol  ldl_direct  \\\n",
       "5841951         NaN        NaN          NaN              NaN         NaN   \n",
       "\n",
       "         dilution_factor  \n",
       "5841951              NaN  \n",
       "\n",
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       "## SIRE"
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       "      <td>-0.229609</td>\n",
       "      <td>-2.127490</td>\n",
       "      <td>...</td>\n",
       "      <td>0.069602</td>\n",
       "      <td>0.016054</td>\n",
       "      <td>Do not know</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.0</td>\n",
       "      <td>7.525</td>\n",
       "      <td>1.454</td>\n",
       "      <td>5.257</td>\n",
       "      <td>1.002230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4437113</th>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>191.9510</td>\n",
       "      <td>35.26380</td>\n",
       "      <td>-3.576160</td>\n",
       "      <td>-11.586000</td>\n",
       "      <td>0.791290</td>\n",
       "      <td>-0.611319</td>\n",
       "      <td>6.333500</td>\n",
       "      <td>...</td>\n",
       "      <td>0.467900</td>\n",
       "      <td>0.026610</td>\n",
       "      <td>Do not know</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51.0</td>\n",
       "      <td>6.758</td>\n",
       "      <td>1.419</td>\n",
       "      <td>4.656</td>\n",
       "      <td>1.002680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4655812</th>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>357.6430</td>\n",
       "      <td>67.96950</td>\n",
       "      <td>-0.645448</td>\n",
       "      <td>5.315860</td>\n",
       "      <td>-0.287619</td>\n",
       "      <td>-1.534660</td>\n",
       "      <td>-2.753470</td>\n",
       "      <td>...</td>\n",
       "      <td>0.088178</td>\n",
       "      <td>0.018331</td>\n",
       "      <td>Do not know</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>67.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4859612</th>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>361.4130</td>\n",
       "      <td>70.13880</td>\n",
       "      <td>-4.054470</td>\n",
       "      <td>5.243910</td>\n",
       "      <td>-1.601220</td>\n",
       "      <td>-3.004260</td>\n",
       "      <td>-3.949990</td>\n",
       "      <td>...</td>\n",
       "      <td>0.074508</td>\n",
       "      <td>0.022812</td>\n",
       "      <td>Do not know</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>59.0</td>\n",
       "      <td>6.593</td>\n",
       "      <td>2.007</td>\n",
       "      <td>4.136</td>\n",
       "      <td>1.007860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5380008</th>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>349.8780</td>\n",
       "      <td>67.95960</td>\n",
       "      <td>-6.964600</td>\n",
       "      <td>6.892720</td>\n",
       "      <td>1.030930</td>\n",
       "      <td>-0.534556</td>\n",
       "      <td>-3.003850</td>\n",
       "      <td>...</td>\n",
       "      <td>0.120642</td>\n",
       "      <td>0.011701</td>\n",
       "      <td>Do not know</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.0</td>\n",
       "      <td>4.451</td>\n",
       "      <td>1.791</td>\n",
       "      <td>2.364</td>\n",
       "      <td>1.002600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5561464</th>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>328.5440</td>\n",
       "      <td>61.74590</td>\n",
       "      <td>-7.773170</td>\n",
       "      <td>3.072170</td>\n",
       "      <td>-1.202250</td>\n",
       "      <td>1.232350</td>\n",
       "      <td>-0.329763</td>\n",
       "      <td>...</td>\n",
       "      <td>0.165885</td>\n",
       "      <td>0.013078</td>\n",
       "      <td>Do not know</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54.0</td>\n",
       "      <td>7.152</td>\n",
       "      <td>1.180</td>\n",
       "      <td>4.698</td>\n",
       "      <td>1.007700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5841951</th>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>4001.0</td>\n",
       "      <td>344.1090</td>\n",
       "      <td>64.25230</td>\n",
       "      <td>-8.572640</td>\n",
       "      <td>5.056070</td>\n",
       "      <td>1.019650</td>\n",
       "      <td>0.665122</td>\n",
       "      <td>-3.471680</td>\n",
       "      <td>...</td>\n",
       "      <td>0.106441</td>\n",
       "      <td>0.012949</td>\n",
       "      <td>Caribbean</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17 rows × 55 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         AGE  SEX  SIRE_ID       PC1       PC2        PC3        PC4  \\\n",
       "1168072   48    1      NaN  332.5570  64.79240  -5.603890   1.104930   \n",
       "1529443   65    1      NaN  145.5840  32.05490   1.633380 -15.596400   \n",
       "1595335   47    0      NaN  324.2560  61.50340  -6.104580   4.247780   \n",
       "1804222   48    0      NaN  234.4210  43.83270  -6.893090   0.334550   \n",
       "2143156   59    1      NaN   48.3585   9.29026   1.305160  -1.466670   \n",
       "2953252   68    0      NaN  366.8170  73.20770  -4.702320   6.865320   \n",
       "3169934   52    1      NaN   12.2970   2.64207   6.490200 -30.050100   \n",
       "3361126   63    1      NaN  342.3860  67.32070  -5.742190   6.297100   \n",
       "3749755   62    0      NaN  216.1980  34.86370  11.478800 -31.403000   \n",
       "4045563   66    1      NaN  295.4590  56.53600  -0.801322   0.178833   \n",
       "4100704   49    0      NaN  364.9500  68.63080  -5.263340   4.799190   \n",
       "4437113   51    0      NaN  191.9510  35.26380  -3.576160 -11.586000   \n",
       "4655812   67    1      NaN  357.6430  67.96950  -0.645448   5.315860   \n",
       "4859612   59    0      NaN  361.4130  70.13880  -4.054470   5.243910   \n",
       "5380008   68    0      NaN  349.8780  67.95960  -6.964600   6.892720   \n",
       "5561464   54    1      NaN  328.5440  61.74590  -7.773170   3.072170   \n",
       "5841951   55    0   4001.0  344.1090  64.25230  -8.572640   5.056070   \n",
       "\n",
       "              PC5       PC6        PC7  ...       EUR       SAS         SIRE  \\\n",
       "1168072 -1.771140 -1.708100  -4.806400  ...  0.149456  0.016820  Do not know   \n",
       "1529443  3.017780  0.144049   6.721240  ...  0.596752  0.017971  Do not know   \n",
       "1595335 -2.547880  3.330120  -5.312460  ...  0.150376  0.025810  Do not know   \n",
       "1804222 -1.262950  6.192830   0.671335  ...  0.391297  0.021872  Do not know   \n",
       "2143156 -3.472300 -2.973370   2.413120  ...  0.852833  0.020443  Do not know   \n",
       "2953252 -2.381090  1.147820  -1.191260  ...  0.050319  0.015607  Do not know   \n",
       "3169934  6.336000 -2.255890  17.567900  ...  0.917436  0.030899  Do not know   \n",
       "3361126  0.612043 -0.676849  -1.532790  ...  0.128158  0.012939  Do not know   \n",
       "3749755  2.753530 -0.307258  10.939800  ...  0.417065  0.039453  Do not know   \n",
       "4045563  0.362361 -3.089330   0.633111  ...  0.236407  0.023383  Do not know   \n",
       "4100704 -0.615779 -0.229609  -2.127490  ...  0.069602  0.016054  Do not know   \n",
       "4437113  0.791290 -0.611319   6.333500  ...  0.467900  0.026610  Do not know   \n",
       "4655812 -0.287619 -1.534660  -2.753470  ...  0.088178  0.018331  Do not know   \n",
       "4859612 -1.601220 -3.004260  -3.949990  ...  0.074508  0.022812  Do not know   \n",
       "5380008  1.030930 -0.534556  -3.003850  ...  0.120642  0.011701  Do not know   \n",
       "5561464 -1.202250  1.232350  -0.329763  ...  0.165885  0.013078  Do not know   \n",
       "5841951  1.019650  0.665122  -3.471680  ...  0.106441  0.012949    Caribbean   \n",
       "\n",
       "         SEX_trait  SIRE_trait  AGE_trait  cholesterol  hdl_cholesterol  \\\n",
       "1168072        1.0         NaN       48.0        5.431            1.098   \n",
       "1529443        1.0         NaN       65.0        5.186            1.045   \n",
       "1595335        0.0         NaN       47.0        2.808            1.431   \n",
       "1804222        0.0         NaN       48.0        5.481            1.370   \n",
       "2143156        1.0         NaN       59.0        6.587            1.424   \n",
       "2953252        0.0         NaN       68.0        4.763            1.531   \n",
       "3169934        1.0         NaN       52.0        5.522            1.066   \n",
       "3361126        1.0         NaN       63.0        3.386              NaN   \n",
       "3749755        0.0         NaN       62.0        4.612            1.084   \n",
       "4045563        1.0         NaN       66.0        6.948            1.006   \n",
       "4100704        0.0         NaN       49.0        7.525            1.454   \n",
       "4437113        0.0         NaN       51.0        6.758            1.419   \n",
       "4655812        1.0         NaN       67.0          NaN              NaN   \n",
       "4859612        0.0         NaN       59.0        6.593            2.007   \n",
       "5380008        0.0         NaN       68.0        4.451            1.791   \n",
       "5561464        1.0         NaN       54.0        7.152            1.180   \n",
       "5841951        NaN         NaN        NaN          NaN              NaN   \n",
       "\n",
       "         ldl_direct  dilution_factor  \n",
       "1168072       3.420         0.993463  \n",
       "1529443       3.370         1.002980  \n",
       "1595335       1.325         1.004270  \n",
       "1804222       3.520         1.001170  \n",
       "2143156       4.233         1.000740  \n",
       "2953252       3.021         1.005700  \n",
       "3169934       3.598         1.008170  \n",
       "3361126       1.803         0.987492  \n",
       "3749755       2.877         0.999714  \n",
       "4045563       4.305         0.997054  \n",
       "4100704       5.257         1.002230  \n",
       "4437113       4.656         1.002680  \n",
       "4655812         NaN              NaN  \n",
       "4859612       4.136         1.007860  \n",
       "5380008       2.364         1.002600  \n",
       "5561464       4.698         1.007700  \n",
       "5841951         NaN              NaN  \n",
       "\n",
       "[17 rows x 55 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import display, HTML, Markdown\n",
    "# make sure two phenotype file matches\n",
    "meta_info = pd.read_csv(\"00_data/meta_info.csv\", index_col=0)\n",
    "meta_info.index = meta_info.index.astype(str)\n",
    "merged = meta_info.join(trait_pheno, rsuffix='_trait')\n",
    "\n",
    "for col in [\"SEX\", \"AGE\"]:\n",
    "    display(Markdown(f\"## {col}\"))\n",
    "    display(merged[merged[f\"{col}_trait\"] != merged[col]])\n",
    "display(Markdown(f\"## SIRE\"))\n",
    "display(merged[merged[\"SIRE_trait\"] != merged[\"SIRE_ID\"]])\n",
    "# these mismatch individuals correspond to missing phenotype\n",
    "# will cope with these in association\n",
    "merged.to_csv(\"00_data/trait_pheno.csv\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
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}